https://github.com/rijonshahariar/doctr
https://github.com/rijonshahariar/doctr
Last synced: 14 days ago
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- Host: GitHub
- URL: https://github.com/rijonshahariar/doctr
- Owner: rijonshahariar
- Created: 2025-06-11T15:58:51.000Z (4 months ago)
- Default Branch: main
- Last Pushed: 2025-08-09T04:05:39.000Z (about 2 months ago)
- Last Synced: 2025-08-29T21:50:48.040Z (about 1 month ago)
- Language: JavaScript
- Size: 936 KB
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Medical Symptom Diagnosis Chatbot
A web-based chatbot that predicts diseases from user-input symptoms using explainable AI (SHAP). The system provides interpretable explanations for its predictions, helping users understand why certain diseases were predicted based on their symptoms.
## Features
- Symptom input via checkboxes or free text
- Disease prediction using decision tree classifier
- SHAP-based explanations for predictions
- Interactive chat interface
- User feedback collection
- Responsive design using Tailwind CSS
## Prerequisites
- Python 3.8+
- pip (Python package manager)
## Installation
1. Clone this repository
2. Install the required dependencies:
```bash
pip install flask pandas numpy scikit-learn shap joblib matplotlib plotly
```
## Project Structure
- `app.py`: Flask backend with model training and API endpoints
- `index.html`: Frontend interface with chat functionality
- `DiseaseAndSymptoms.csv`: Dataset for symptom-disease mapping
- `symptom_Description.csv`: Symptom descriptions
- `decision_tree_model.pkl`: Trained model (generated on first run)
## Running the Application
1. Start the Flask server:
```bash
python app.py
```
2. Open your browser and navigate to:
```
http://localhost:5000
```
## API Endpoints
- `/predict` (POST): Predict disease from symptoms
- `/feedback` (POST): Submit user feedback
- `/symptoms` (GET): Get list of all possible symptoms
## Dataset Information
The project uses the Disease Symptom Prediction dataset with approximately 4,000 symptom-disease pairs. The dataset is preprocessed to map binary symptom columns to disease targets.
## Disclaimer
This tool is not a substitute for professional medical advice. Always consult with healthcare professionals for medical decisions.
## Privacy
- No permanent storage of user inputs
- Feedback is logged anonymously
- No external API calls or data sharing
## Development
The project is built with:
- Backend: Python, Flask, scikit-learn, SHAP
- Frontend: HTML, Tailwind CSS, JavaScript
- Visualization: Matplotlib/Plotly for SHAP plots